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Improve comments, exceptions, typing and init of OpenAI model code

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Debanjum Singh Solanky 2023-01-07 21:56:00 -03:00
parent c0ae8eee99
commit 6119005838
1 changed files with 17 additions and 7 deletions
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24
src/utils/models.py
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@ -4,25 +4,35 @@ import torch
from tqdm import trange
# Internal Packages
from src.utils.state import processor_config
from src.utils.state import processor_config, config_file
class OpenAI:
def __init__(self, model_name, device=None):
self.model_name = model_name
if not processor_config or not processor_config.conversation or not processor_config.conversation.openai_api_key:
raise Exception(f"Set OpenAI API key under processor-config > conversation > openai-api-key in config file: {config_file}")
openai.api_key = processor_config.conversation.openai_api_key
self.embedding_dimensions = 1536 # Default to embedding dimensions of text-embedding-ada-002 model
self.embedding_dimensions = None
def encode(self, entries, device=None, **kwargs):
def encode(self, entries: list[str], device=None, **kwargs):
embedding_tensors = []
for index in trange(0, len(entries)):
# OpenAI models create better embeddings for entries without newlines
processed_entry = entries[index].replace('\n', ' ')
try:
response = openai.Embedding.create(input=entries[index], model=self.model_name)
response = openai.Embedding.create(input=processed_entry, model=self.model_name)
embedding_tensors += [torch.tensor(response.data[0].embedding, device=device)]
self.embedding_dimensions = len(response.data[0].embedding) # Set embedding dimensions to this model's
# Use current models embedding dimension, once available
# Else default to embedding dimensions of the text-embedding-ada-002 model
self.embedding_dimensions = len(response.data[0].embedding) if not self.embedding_dimensions else 1536
except Exception as e:
print(f"Failed to encode entry {index} of length: {len(entries[index])}\n\n{entries[index][:1000]}...\n\n{e}")
# Use zero embedding vector for entries with failed embeddings
# This ensures entry embeddings match the order of the source entries
# And they have minimal similarity to other entries (as zero vectors are always orthogonal to other vector)
embedding_tensors += [torch.zeros(self.embedding_dimensions, device=device)]
return torch.stack(embedding_tensors)